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Analysis of amino acid sequence variations and immunoglobulin E-binding epitopes of German cockroach tropomyosin.

ABSTRACT: The allergenicities of tropomyosins from different organisms have been reported to vary. The cDNA encoding German cockroach tropomyosin (Bla g 7) was isolated, expressed, and characterized previously. In the present study, the amino acid sequence variations in German cockroach tropomyosin were analyzed in order to investigate its influence on allergenicity. We also undertook the identification of immunodominant peptides containing immunoglobulin E (IgE) epitopes which may facilitate the development of diagnostic and immunotherapeutic strategies based on the recombinant proteins. Two-dimensional gel electrophoresis and immunoblot analysis with mouse anti-recombinant German cockroach tropomyosin serum was performed to investigate the isoforms at the protein level. Reverse transcriptase PCR (RT-PCR) was applied to examine the sequence diversity. Eleven different variants of the deduced amino acid sequences were identified by RT-PCR. German cockroach tropomyosin has only minor sequence variations that did not seem to affect its allergenicity significantly. These results support the molecular basis underlying the cross-reactivities of arthropod tropomyosins. Recombinant fragments were also generated by PCR, and IgE-binding epitopes were assessed by enzyme-linked immunosorbent assay. Sera from seven patients revealed heterogeneous IgE-binding responses. This study demonstrates multiple IgE-binding epitope regions in a single molecule, suggesting that full-length tropomyosin should be used for the development of diagnostic and therapeutic reagents.

SUBMITTER: Jeong KY

PROVIDER: S-EPMC515276 | biostudies-literature | 2004 Sep

REPOSITORIES: biostudies-literature

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Analysis of amino acid sequence variations and immunoglobulin E-binding epitopes of German cockroach tropomyosin.

Jeong Kyoung Yong KY Lee Jongweon J Lee In-Yong IY Ree Han-Il HI Hong Chein-Soo CS Yong Tai-Soon TS

Clinical and diagnostic laboratory immunology 20040901 5

The allergenicities of tropomyosins from different organisms have been reported to vary. The cDNA encoding German cockroach tropomyosin (Bla g 7) was isolated, expressed, and characterized previously. In the present study, the amino acid sequence variations in German cockroach tropomyosin were analyzed in order to investigate its influence on allergenicity. We also undertook the identification of immunodominant peptides containing immunoglobulin E (IgE) epitopes which may facilitate the developm ...[more]

PMID: 15358646

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Project description:Understanding the molecular details of protein-DNA interactions is critical for deciphering the mechanisms of gene regulation. We present a machine learning approach for the identification of amino acid residues involved in protein-DNA interactions.We start with a Naïve Bayes classifier trained to predict whether a given amino acid residue is a DNA-binding residue based on its identity and the identities of its sequence neighbors. The input to the classifier consists of the identities of the target residue and 4 sequence neighbors on each side of the target residue. The classifier is trained and evaluated (using leave-one-out cross-validation) on a non-redundant set of 171 proteins. Our results indicate the feasibility of identifying interface residues based on local sequence information. The classifier achieves 71% overall accuracy with a correlation coefficient of 0.24, 35% specificity and 53% sensitivity in identifying interface residues as evaluated by leave-one-out cross-validation. We show that the performance of the classifier is improved by using sequence entropy of the target residue (the entropy of the corresponding column in multiple alignment obtained by aligning the target sequence with its sequence homologs) as additional input. The classifier achieves 78% overall accuracy with a correlation coefficient of 0.28, 44% specificity and 41% sensitivity in identifying interface residues. Examination of the predictions in the context of 3-dimensional structures of proteins demonstrates the effectiveness of this method in identifying DNA-binding sites from sequence information. In 33% (56 out of 171) of the proteins, the classifier identifies the interaction sites by correctly recognizing at least half of the interface residues. In 87% (149 out of 171) of the proteins, the classifier correctly identifies at least 20% of the interface residues. This suggests the possibility of using such classifiers to identify potential DNA-binding motifs and to gain potentially useful insights into sequence correlates of protein-DNA interactions.Naïve Bayes classifiers trained to identify DNA-binding residues using sequence information offer a computationally efficient approach to identifying putative DNA-binding sites in DNA-binding proteins and recognizing potential DNA-binding motifs.

Project description:BackgroundThe identification of immunogenic regions on the surface of antigens, which are able to be recognized by antibodies and to trigger an immune response, is a major challenge for the design of new and effective vaccines. The prediction of such regions through computational immunology techniques is a challenging goal, which will ultimately lead to a drastic limitation of the experimental tests required to validate their efficiency. However, current methods are far from being sufficiently reliable and/or applicable on a large scale.ResultsWe developed SEPIa, a B-cell epitope predictor from the protein sequence, which is sufficiently fast to be applicable on a large scale. The originality of SEPIa lies in the combination of two classifiers, a naïve Bayesian and a random forest classifier, through a voting algorithm that exploits the advantages of both. It is based on 13 sequence-based features, whose values in a 9-residue sequence window are compiled to predict the epitope/non-epitope state of the central residue. The features are related to the type of amino acid, its conservation in homologous proteins, and its tendency of being exposed to the solvent, soluble, flexible, and disordered. The highest signal is obtained from statistical amino acid preferences, but all 13 features contribute non-negligibly in the predictor. SEPIa's average prediction accuracy is limited, with an AUC score (area under the receiver operating characteristic curve) that reaches 0.65 both in 10-fold cross-validation and on an independent test set. It is nevertheless slightly higher than that of other methods evaluated on the same test set.ConclusionsSEPIa was applied to a test protein whose epitopes are known, human ?2 adrenergic G-protein-coupled receptor, with promising results. Although the actual AUC score is rather low, many of the predicted epitopes cluster together and overlap the experimental epitope region. The reasons underlying the limitations of SEPIa and of all other B-cell epitope predictors are discussed.

Dataset Information

Analysis of amino acid sequence variations and immunoglobulin E-binding epitopes of German cockroach tropomyosin.

Publications

Analysis of amino acid sequence variations and immunoglobulin E-binding epitopes of German cockroach tropomyosin.

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